






"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 	a		(   __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					KandinskyVaaInpaintPipeline
    SCREAMING_SNAKE_CASE		:	str		=					["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
    SCREAMING_SNAKE_CASE		:	int		=					[
        """image_embeds""",
        """negative_image_embeds""",
        """image""",
        """mask_image""",
    ]
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					[
        """generator""",
        """height""",
        """width""",
        """latents""",
        """guidance_scale""",
        """num_inference_steps""",
        """return_dict""",
        """guidance_scale""",
        """num_images_per_prompt""",
        """output_type""",
        """return_dict""",
    ]
    SCREAMING_SNAKE_CASE		:	Any		=					False






    @property
    def   UpperCamelCase			(			self	:				int   )	->						Optional[int]:
      return 32






    @property
    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						int:
      return 32






    @property
    def   UpperCamelCase			(			self	:				Tuple   )	->						str:
      return self.time_input_dim






    @property
    def   UpperCamelCase			(			self	:				int   )	->						Optional[Any]:
      return self.time_input_dim * 4






    @property
    def   UpperCamelCase			(			self	:				List[Any]   )	->						Optional[Any]:
      return 100






    @property
    def   UpperCamelCase			(			self	:				Tuple   )	->						Optional[Any]:
      torch.manual_seed(0   )

      lowerCamelCase_       	=		{
          'in_channels': 9,
          # Out channels is double in channels because predicts mean and variance
          'out_channels': 8,
          'addition_embed_type': 'image',
          'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
          'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
          'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
          'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
          'layers_per_block': 1,
          'encoder_hid_dim': self.text_embedder_hidden_size,
          'encoder_hid_dim_type': 'image_proj',
          'cross_attention_dim': self.cross_attention_dim,
          'attention_head_dim': 4,
          'resnet_time_scale_shift': 'scale_shift',
          'class_embed_type': None,
      }

      lowerCamelCase_       	=		UNetaDConditionModel(**__SCREAMING_SNAKE_CASE   )
      return model






    @property
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Tuple:
      return {
          "block_out_channels": [32, 64],
          "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 12,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }






    @property
    def   UpperCamelCase			(			self	:				Dict   )	->						Tuple:
      torch.manual_seed(0   )
      lowerCamelCase_       	=		VQModel(**self.dummy_movq_kwargs   )
      return model






    def   UpperCamelCase			(			self	:				List[Any]   )	->						str:
      lowerCamelCase_       	=		self.dummy_unet
      lowerCamelCase_       	=		self.dummy_movq

      lowerCamelCase_       	=		DDIMScheduler(
          num_train_timesteps=1000			,		beta_schedule='linear'			,		beta_start=0.00_085			,		beta_end=0.012			,		clip_sample=__SCREAMING_SNAKE_CASE			,		set_alpha_to_one=__SCREAMING_SNAKE_CASE			,		steps_offset=1			,		prediction_type='epsilon'			,		thresholding=__SCREAMING_SNAKE_CASE			,		)

      lowerCamelCase_       	=		{
          'unet': unet,
          'scheduler': scheduler,
          'movq': movq,
      }

      return components






    def   UpperCamelCase			(			self	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]=0   )	->						int:
      lowerCamelCase_       	=		floats_tensor((1, self.text_embedder_hidden_size)			,		rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		floats_tensor((1, self.text_embedder_hidden_size)			,		rng=random.Random(seed + 1   )   ).to(
          __SCREAMING_SNAKE_CASE   )
      # create init_image
      lowerCamelCase_       	=		floats_tensor((1, 3, 64, 64)			,		rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		image.cpu().permute(0			,		2			,		3			,		1   )[0]
      lowerCamelCase_       	=		Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert('RGB'   ).resize((256, 256)   )
      # create mask
      lowerCamelCase_       	=		np.ones((64, 64)			,		dtype=np.floataa   )
      lowerCamelCase_       	=		0

      if str(__SCREAMING_SNAKE_CASE   ).startswith('mps'   ):
        lowerCamelCase_       	=		torch.manual_seed(__SCREAMING_SNAKE_CASE   )
      else:
        lowerCamelCase_       	=		torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		{
          'image': init_image,
          'mask_image': mask,
          'image_embeds': image_embeds,
          'negative_image_embeds': negative_image_embeds,
          'generator': generator,
          'height': 64,
          'width': 64,
          'num_inference_steps': 2,
          'guidance_scale': 4.0,
          'output_type': 'np',
      }
      return inputs






    def   UpperCamelCase			(			self	:				Tuple   )	->						Any:
      lowerCamelCase_       	=		'cpu'

      lowerCamelCase_       	=		self.get_dummy_components()

      lowerCamelCase_       	=		self.pipeline_class(**__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		pipe.to(__SCREAMING_SNAKE_CASE   )

      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE   )   )
      lowerCamelCase_       	=		output.images

      lowerCamelCase_       	=		pipe(
          **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE   )			,		return_dict=__SCREAMING_SNAKE_CASE			,		)[0]

      lowerCamelCase_       	=		image[0, -3:, -3:, -1]
      lowerCamelCase_       	=		image_from_tuple[0, -3:, -3:, -1]

      print(F'''image.shape {image.shape}'''   )

      assert image.shape == (1, 64, 64, 3)

      lowerCamelCase_       	=		np.array(
          [0.50_775_903, 0.49_527_195, 0.48_824_543, 0.50_192_237, 0.48_644_906, 0.49_373_814, 0.4_780_598, 0.47_234_827, 0.48_327_848]   )

      assert (
          np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
      ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2
      ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






    def   UpperCamelCase			(			self	:				List[str]   )	->						Union[str, Any]:
      super().test_inference_batch_single_identical(expected_max_diff=3e-3   )






@slow
@require_torch_gpu
class 	a		(   unittest.TestCase						):






    def   UpperCamelCase			(			self	:				List[Any]   )	->						Any:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






    def   UpperCamelCase			(			self	:				int   )	->						Union[str, Any]:
      lowerCamelCase_       	=		load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'   )

      lowerCamelCase_       	=		load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'   )
      lowerCamelCase_       	=		np.ones((768, 768)			,		dtype=np.floataa   )
      lowerCamelCase_       	=		0

      lowerCamelCase_       	=		'a hat'

      lowerCamelCase_       	=		KandinskyVaaPriorPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-2-prior'			,		torch_dtype=torch.floataa   )
      pipe_prior.to(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		KandinskyVaaInpaintPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-2-decoder-inpaint'			,		torch_dtype=torch.floataa   )
      lowerCamelCase_       	=		pipeline.to(__SCREAMING_SNAKE_CASE   )
      pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		torch.Generator(device='cpu'   ).manual_seed(0   )
      lowerCamelCase_	,      lowerCamelCase_       	=		pipe_prior(
          __SCREAMING_SNAKE_CASE			,		generator=__SCREAMING_SNAKE_CASE			,		num_inference_steps=5			,		negative_prompt=''			,		).to_tuple()

      lowerCamelCase_       	=		pipeline(
          image=__SCREAMING_SNAKE_CASE			,		mask_image=__SCREAMING_SNAKE_CASE			,		image_embeds=__SCREAMING_SNAKE_CASE			,		negative_image_embeds=__SCREAMING_SNAKE_CASE			,		generator=__SCREAMING_SNAKE_CASE			,		num_inference_steps=100			,		height=768			,		width=768			,		output_type='np'			,		)

      lowerCamelCase_       	=		output.images[0]

      assert image.shape == (768, 768, 3)

      assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )












"""simple docstring"""





def 	lowerCamelCase__			(				_lowerCamelCase			:						float						,							_lowerCamelCase			:						float					) ->							float:

  if density <= 0:
    raise ValueError('Impossible fluid density'					)
  if bulk_modulus <= 0:
    raise ValueError('Impossible bulk modulus'					)

  return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
  import doctest

  doctest.testmod()




